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METAGENOMICS: Sequencing the
unknown
Paula González
INTRODUCTION
Metagenomics is the study of
a collection of genetic material
from a mixed community of
organisms, usually referring to
microbial communities.
Functional analysis
Metabolic profiling
Sequencing
Metagenomic dataset
Taxonomic profiling
INTRODUCTION
A typical current computational
meta’omic pipeline to analyze and contrast
microbial communities
SEQUENCING
Extract DNA or RNA from a microbial community in its entirety.
Library construction and short-read sequencing of genomes/transcripts.
Size and depth coverage of genome assemblies
Information on relative species abundance
Illumina platform is preferred for meta’omic sequencing.
TAXONOMIC PROFILING
Two different approaches:
•Intrinsic: From reference genomes, the sequences-free classifier are used to bin new meta’omic reads. Whole-genome searches.
•Extrinsic: Compare metagenomic sequences with reference sequences in order to identify phylogenetic origin. Marker-based
approaches.
Phylogenetic tree of rhodopsinlike
genes in the Sargasso Sea data
FUNCTIONAL ANALYSIS
Linking sequence to function:
•Similarities between discovered genes and those in the databases
•Predicting secondary structure and recognizing protein motifs
Limitations:
•Analysis of proteins need: large quantities, purified and even crystallized
•Completeness of existing data
Solution:
SCREEN THE METAGENOMIC
LIBRARIES DIRECTLY FOR
EXPRESSED FUNCTIONS
(Function-driven metagenomics)
ASSOCIATION NETWORKS
Understand the microbes ecology, environmental response and interorganismal interactions: interspecies and intercellular relationships.
Bioinformatic challenge. Different approaches get only descriptive covariation.
Interactions?
MODELS OF MICROBIOME EVOLUTION
Evolution within microbial communities can occur on two different time scales:
• Over the course of millennia: structure of host-associated communities evolve more slowly in tandem with their hosts’ physiology and immune systems.
• Over the course of days, weeks or years: microbial genome plasticity allows remarkably rapid acquisitions of novel mutations and laterally transferred genes.
Rhizobia infection
in root development
PREDICTIVE BIOINFORMATIC MODELS
Ultimate goals: to develop predictive models of the whole-community response to changing stimuli.
Fist attempts: Artificial neural network Predict ocean bacterial community
Mechanistic, relying on joint metabolic networks
Descriptive systems biology of microbial physiological “rules”
MODELS
SARGASSO SEA
An ocean environment thought to be relatively low in diversity.
Its microbial community is one of the largest metagenomic sequencing project.
Reported 1.214.207 protein-encoding genes, 10 times more protein sequences than were present in databases.
Underscore the significant challenges and opportunities associated with archiving, integrating and analyzing metagenomic sequence databases.
Allowed studies of whole-genome genomic variability, structural
organization and evolution in taxa.
CONCLUSIONS AND OUTLOOK
Whole-genome metagenomic shotgun sequencing validates experimental and bioinformatic procedures to answer typical biological questions.
Statistical methods for biomarker discovery and phenotype prediction can be performed with the data provided.
Next step in microbial community systems biology will be to integrate and meta-analyze multiple metagenomic data sets.
METATRANSCRIPTOMICS
METAPROTEOMICS
METABOLOMICS
METAGENOMICS
INTEGRATION
BIBLIOGRAPHY
http://ghr.nlm.nih.gov/glossary=metagenomics
Handelsman, J., et al. "The new science of metagenomics: Revealing the secrets of our microbial planet." p. 47- 84. Nat Res Council Report 13
(2007).
Segata, Nicola, et al. "Computational meta'omics for microbial community studies." Molecular systems biology 9.1 (2013): 666.
Gill, Steven R., et al. "Metagenomic analysis of the human distal gut microbiome." science 312.5778 (2006): 1355-1359.
Venter, J. Craig, et al. "Environmental genome shotgun sequencing of the Sargasso Sea." science 304.5667 (2004): 66-74.